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The paper introduces Signals and Queries (S&Q), a lightweight first-stage relevance filter for Parlant 4.0 that uses offline-trained logistic classifiers on LLM-generated examples to cut at least 30% of candidate behavioral policies per turn while preserving high recall, outperforming top-K and cosine-threshold retrieval.